The first generation DNA Sequencing

Transcription

1 The first generation DNA Sequencing Slides 3 17 are modified from faperta.ugm.ac.id/newbie/download/pak_tar/.../instrument20072.ppt slides are from Chengxiang Zhai at UIUC.

2 The strand direction

3 DNA sequencing Determination of nucleotide sequence the determination of the precise sequence of nucleotides in a sample of DNA Two similar methods: 1. Maxam and Gilbert method 2. Sanger method They depend on the production of a mixture of oligonucleotides labeled either radioactively or fluorescein, with one common end and differing in length by a single nucleotide at the other end This mixture of oligonucleotides is separated by high resolution electrophoresis on polyacrilamide gels and the position of the bands determined

4 Maxam-Gilbert Walter Gilbert Harvard physicist Knew James Watson Became intrigued with the biological side Became a biophysicist Allan Maxam

5 The Maxam-Gilbert Technique Principle - Chemical Degradation of Purines Purines (A, G) damaged by dimethylsulfate Methylation of base Heat releases base Alkali cleaves G Dilute acid cleave A>G

6 Maxam-Gilbert Technique Principle Chemical Degradation of Pyrimidines Pyrimidines (C, T) are damaged by hydrazine Piperidine cleaves the backbone 2 M NaCl inhibits the reaction with T

7 Advantages/disadvantages Maxam-Gilbert sequencing Requires lots of purified DNA, and many intermediate purification steps Relatively short readings Automation not available (sequencers) Remaining use for footprinting (partial protection against DNA modification when proteins bind to specific regions, and that produce holes in the sequence ladder) In contrast, the Sanger sequencing methodology requires little if any DNA purification, no restriction digests, and no labeling of the DNA sequencing template

8 Fred Sanger, 1958 Was originally a protein chemist Made his first mark in sequencing proteins Made his second mark in sequencing RNA 1980 dideoxy sequencing Sanger Method

9 Sanger Method in-vitro DNA synthesis using terminators, use of dideoxynucleotides that do not permit chain elongation after their integration DNA synthesis using deoxy- and dideoxynucleotides that results in termination of synthesis at specific nucleotides Requires a primer, DNA polymerase, a template, a mixture of nucleotides, and detection system Incorporation of di-deoxynucleotides into growing strand terminates synthesis Synthesized strand sizes are determined for each dideoxynucleotide by using gel or capillary electrophoresis Enzymatic methods

10 deoxyribonucleotide

11 Dideoxynucleotide PPP O 5 CH2 O BASE 3 no hydroxyl group at 3 end prevents strand extension

12 primer 3 CCGTAC dntp ddatp ddttp ddctp ddgtp GGCA GGCAT A T C G GGC G GG GGCATG

13

14 Sample Output 1 lane

15 Phred

16 Sanger sequencing Laser excitation of fluorescent labels as fragments of discreet lengths exit the capillary, coupled to four color detection of emission spectra, provides the readout that is represented in a Sanger sequencing trace. Software translates these traces into DNA sequence, while also generating error probabilities for each base call. Simultaneous electrophoresis in 96 or 384 independent capillaries provides a limited level of parallelization. After three decades of gradual improvement, the Sanger biochemistry can be applied to achieve read lengths of up to ~1,000 bp, and per base raw accuracies as high as %. In the context of highthroughput shotgun genomic sequencing, Sanger sequencing costs on the order of $0.50 per kilobase.

17 Comparison Sanger Method Enzymatic Requires DNA synthesis Termination of chain elongation Maxam Gilbert Method Chemical Requires DNA Requires long stretches of DNA Breaks DNA at different nucleotides

18 How to obtain the human genome sequence The Sanger sequencing can only generate 1kb long DNA segments. How to obtain the human genome that are 3 billion letters? The answer is to get pieces of DNA segments and assemble them into the genome.

19 Challenges with Fragment Assembly Sequencing errors ~1 2% of bases are wrong Repeats false overlap due to repeat Bacterial genomes:5% Mammals: 50%

20 Repeat Types Low-Complexity DNA (e.g. ATATATATACATA ) Microsatellite repeats (a 1 a k ) N where k ~ 3 6 (e.g. CAGCAGTAGCAGCACCAG) Transposons/retrotransposons SINE Short Interspersed Nuclear Elements (e.g., Alu: ~300 bp long, 10 6 copies) LINE Long Interspersed Nuclear Elements ~500 5,000 bp long, 200,000 copies LTR retroposons Long Terminal Repeats (~700 bp) at each end Gene Families genes duplicate & then diverge Segmental duplications ~very long, very similar copies

21 Strategies for whole genome sequencing 1. Hierarchical Clone by clone yeast, worm, human i. Break genome into many long fragments ii. Map each long fragment onto the genome iii. Sequence each fragment with shotgun 2. Online version of (1) Walking rice genome i. Break genome into many long fragments ii. Start sequencing each fragment with shotgun iii. Construct map as you go 3. Whole Genome Shotgun fly, human, mouse, rat, fugu One large shotgun pass on the whole genome

22 Hierarchical Sequencing vs. Whole Genome Shotgun Hierarchical Sequencing Advantages: Easy assembly Disadvantages: Build library & physical map; Redundant sequencing Whole Genome Shotgun (WGS) Advantages: No mapping, no redundant sequencing Disadvantages: Difficult to assemble and resolve repeats Whole Genome Shotgun appears to get more popular

23 Whole Genome Shotgun Sequencing genome cut many times at random known dist forward-reverse paired reads ~500 bp ~500 bp

24 Fragment Assembly reads Cover region with ~7-fold redundancy Overlap reads and extend to reconstruct the original genomic region

25 Read Coverage C Length of genomic segment: Number of reads: Length of each read: G N L Definition: Coverage C = NL/ G

26 Enough Coverage How much coverage is enough? According to the Lander Waterman model: Assuming uniform distribution of reads, C=7 results in 1 gap per 1,000 nucleotides

27 Lander Waterman Model Major Assumptions Reads are randomly distributed in the genome The number of times a base is sequenced follows a Poisson distribution px ( x) Average times x! G= genome length, L=read length, N = # reads Mean of Poisson: =LN/G (coverage) % bases not sequenced: p(x=0) = = 0.09% Total gap length: p(x=0)*g Total number of gaps: p(x=0)*n Implications x e This model was used to plan the Human Genome Project

28 Overlap Layout Consensus Assemblers: ARACHNE, PHRAP, CAP, TIGR, CELERA Overlap: find potentially overlapping reads Layout: merge reads into contigs and contigs into supercontigs Consensus: derive the DNA sequence and correct read errors..acgattacaataggtt..

29 Overlap Find the best match between the suffix of one read and the prefix of another Due to sequencing errors, need to use dynamic programming to find the optimal overlap alignment Apply a filtration method to filter out pairs of fragments that do not share a significantly long common substring

30 Overlapping Reads Sort all k mers in reads (k ~ 24) Find pairs of reads sharing a k-mer Extend to full alignment throw away if not >95% similar TACA TAGATTACACAGATTACT GA TAGT TAGATTACACAGATTACTAGA

31 Overlapping Reads and Repeats A k mer that appears N times, initiates N 2 comparisons For an Alu that appears 10 6 times comparisons too much Solution: Discard all k mers that appear more than t Coverage, (t ~ 10)

32 Finding Overlapping Reads Create local multiple alignments from the overlapping reads TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA

33 Finding Overlapping Reads (cont d) Correct errors using multiple alignment TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA C: 20 C: 35 T: 30 C: 35 C: 40 A: 15 A: 25 - A: 40 A: 25 Score alignments Accept alignments with good scores C: 20 C: 35 C: 0 C: 35 C: 40 A: 15 A: 25 A: 0 A: 40 A: 25 Multiple alignments will be covered later in the course

34 Layout Repeats are a major challenge Do two aligned fragments really overlap, or are they from two copies of a repeat?

35 Merge Reads into Contigs repeat region Merge reads up to potential repeat boundaries

36 Merge Reads into Contigs (cont d) repeat region Ignore non maximal reads Merge only maximal reads into contigs

37 Merge Reads into Contigs (cont d) repeat boundary??? sequencing error a b Ignore hanging reads, when detecting repeat boundaries

38 Merge Reads into Contigs (cont d)????? Unambiguous Insert non-maximal reads whenever unambiguous

39 Link Contigs into Supercontigs Normal density Too dense: Overcollapsed? (Myers et al. 2000) Inconsistent links: Overcollapsed?

40 Link Contigs into Supercontigs (cont d) Find all links between unique contigs Connect contigs incrementally, if 2 links

41 Link Contigs into Supercontigs (cont d) Fill gaps in supercontigs with paths of overcollapsed contigs

42 Consensus A consensus sequence is derived from a profile of the assembled fragments A sufficient number of reads is required to ensure a statistically significant consensus Reading errors are corrected

43 Derive Consensus Sequence TAGATTACACAGATTACTGA TTGATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAAACTA TAG TTACACAGATTATTGACTTCATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGGGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAA CTA Derive multiple alignment from pairwise read alignments Derive each consensus base by weighted voting